While both methods are capable of generating more realistic results than approaches that solely rely on \(\ell _2\)  loss, they have some inherent shortcomings.
The first paradigm typically trains the SR generator from scratch using a combined objective function consisting of a fidelity term and an adversarial loss.
In this setting, the generator is responsible for both capturing the natural image characteristics and maintaining the fidelity to the ground-truth. This inevitably limits the capability of approximating the natural image manifold. As a result, these methods often produce artifacts and unnatural textures.
As shown in Fig. REF , while ESRGAN [1]} faithfully recovers the structures ( pose, ear shape) of the cat, it struggles to produce realistic textures.
